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THÈSE présentée par Rodrigo MENA pour l’obtention du GRADE DE DOCTEUR Spécialité: Génie Industriel Laboratoire d’accueil: Laboratoire de Génie Industriel SUJET: Risk–based Modeling, Simulation and Optimization for the Integration of Renewable Distributed Generation into Electric Power Networks Modélisation, simulation et optimisation basée sur le risque pour l’intégration de génération distribuée renouvelable dans des réseaux de puissance électrique soutenue le: 30 juin 2015 devant un jury composé de: Enrique DROGUETT University of Maryland Reviewer Federica FOIADELLI Politecnico di Milano Reviewer Michel MINOUX Université Pierre et Marie CURIE Examiner Guillaume SANDOU CentraleSupélec Examiner Vera SILVA Électricité de France R&D Examiner Enrico ZIO CentraleSupélec Supervisor v Martin HENNEBEL CentraleSupélec Co–supervisor Yan–Fu LI CentraleSupélec Co–supervisor 2015ECAP0034 To my brother . Acknowledgements I would like to acknowledge my thesis supervisor, Professor Enrico Zio, for having given me the opportunity to develop this Ph.D. work. Thanks for trusting me, for all the guidance and advice to improve my knowledge of how to do forefront research, for constantly spreading enthusiasm and eagerness in learning and for having encouraged me to overcome the difficulties encountered over these three years. To me, It has been a privilege to work in Prof. Zio’s team and undoubtedly a real fruitful experience, both professionally and personally. I extend my appreciation to my co–supervisors, Dr. Yan–fu Li and Dr. Martin Hennebel, and to a former collaborator of the Chair SSEC, Dr. Carlos Ruiz, whose generous support has been fundamental in the development of this work. I am grateful to you for having been always interested and attentive in following the progress of this thesis, for the willingness and patience to discuss and teach me whenever I needed technical counsel. To all the members of the jury, Professors Federica Foiadelli, Guillaume Sandou, Michel Minoux and Enrique Droguett, and Dr. Vera Silva, my sincere gratitude for your time and consideration. It was an absolute challenge having defended my thesis in front of such excelled academics and professionals. I thank you all for the constructive remarks, comments and suggestions and, in particular, Prof. Foiadelli and Prof. Droguett that kindly accepted to be the reviewers of the manuscript. My warmest gratitude and recognition go to Corinne Ollivier, Delphine Martin and Sylvie Guillemain, the three loving assistants (a.k.a. supreme bosses) of the Industrial Engineering Department (LGI), because of their unconditional good will to help us, the usually lost–in–translation Ph.D. students. In addition, I would like to thank my friends and colleagues from LGI, for all the unforgettable moments. To conclude, words are not enough to express how beholden I am to my beloved parents, siblings and Elisa, they just know that I am. Abstract Renewable distributed generation (DG) is expected to continue playing a fundamental role in the development and operation of sustainable, efficient and reliable electric power systems, by virtue of offering a practical alternative to diversify and decentralize the overall power generation, benefiting from cleaner and safer energy sources. The integration of renewable DG in the existing electric power networks poses socio–techno–economical challenges, which have attracted substantial research and advancement. In this context, the focus of the present thesis is the design and development of a modeling, simulation and optimization framework for the integration of renewable DG into electric power networks. The specific problem considered is that of selecting the technology, size and locationof renewable generation units, under technical, operational and economic constraints. Within this problem, key research questions to be addressed are: (i) the representation and treatment of the uncertain physical variables (like the availability of diverse primary renewable energy sources, bulk– power supply, power demands and occurrence of components failures) that dynamically determine the DG–integrated network operation, (ii) the propagation of these uncertainties onto the system operational response and the control of the associated risk and (iii) the intensive computational efforts resulting from the complex combinatorial optimization problem of renewable DG integration. For the evaluation of the system with a given plan of renewable DG, a non–sequential Monte Carlo simulation and optimal power flow (MCS–OPF) computational model has been designed and implemented, that emulates the DG–integrated network operation. Random realizations of operational scenarios are generated by sampling from the different uncertain variables distributions, and for each scenario the system performance is evaluated in terms of economics and reliability of power supply, represented by the global cost (CG) and the energy not supplied (ENS), respectively. To measure and control the risk relative to system performance, two indicators are introduced, the conditional value–at–risk (CVaR) and the CVaR deviation (DCVaR). For the optimal technology selection, size and location of the renewable DG units, two distinct multi–objective optimization (MOO) approaches have been implemented by heuristic optimization (HO) search engines. The first approach is based on the fast non–dominated sorting genetic algorithm (NSGA–II) and aims at the concurrent minimization of the expected values of CG and ENS, then ECG and EENS, respectively, combined with their corresponding CVaR(CG) and CVaR(ENS) viii values; the second approach carries out a MOO differential evolution (DE) search to minimize simultaneously ECG and its associated deviation DCVaR(CG). Both optimization approaches embed the MCS–OPF computational model to evaluate the performance of each DG–integrated network proposed by the HO search engine. The challenge coming from the large computational efforts required by the proposed simulation and optimization frameworks has been addressed introducing an original technique, which nests hierarchical clustering analysis (HCA) within a DE search engine. Examples of application of the proposed frameworks have been worked out, regarding an adaptation of the IEEE 13 bus distribution test feeder and a realistic setting of the IEEE 30 bus sub–transmission and distribution test system. The results show that these frameworks are effective in finding optimal DG–integrated networks solutions, while controlling risk from two distinct perspectives: directly through the use of CVaR and indirectly by targeting uncertainty in the form of DCVaR. Moreover, CVaR acts as an enabler of trade–offs between optimal expected performance and risk, and DCVaR integrates also uncertainty into the analysis, providing a wider spectrum of information for well–supported and confident decision making. The main original contributions of the thesis work here presented reside in: framing the problem of optimal technology selection, size and location of renewable generation units, within an integrated simulation and optimization approach that takes into consideration multiple uncertain operational inputs through the developed MCS–OPF, allows assessing and controlling risk by introducing CVaR and DCVaR measures, and copes with computational complexity by embedding HCA into the HO search engine. Keywords: renewable distributed generation, uncertainty, risk, simulation, optimization, condi- tional value–at–risk, conditional value–at–risk deviation, genetic algorithm, differential evolution, hierarchical clustering analysis Résumé Il est prévu que la génération distribuée par l’entremise d’énergie de sources renouvelables (DG) continuera à jouer un rôle clé dans le développement et l’exploitation des systèmes de puissance électrique durables, efficaces et fiables, en vertu de cette fournit une alternative pratique dedécen- tralisation et diversification de la demande globale d’énergie, bénéficiant de sources d’énergie plus propres et plus sûrs. L’intégration de DG renouvelable dans les réseaux électriques existants pose des défis socio–technico–économiques, qu’ont attirés de la recherche et de progrès substantiels. Dans ce contexte, la présente thèse a pour objet la conception et le développement d’un cadre de modélisation, simulation et optimisation pour l’intégration de DG renouvelable dans des réseaux de puissance électrique existants. Le problème spécifique à considérer est celui de la sélection de latech- nologie, la taille et l’emplacement de des unités de génération renouvelable d’énergie, sous des con- traintes techniques, opérationnelles et économiques. Dans ce problème, les questions de recherche clés à aborder sont: (i) la représentation et le traitement des variables physiques incertains (comme la disponibilité de les diverses sources primaires d’énergie renouvelables, l’approvisionnements d’électricité en vrac, la demande de puissance et l’apparition de défaillances de composants) qui déterminent dynamiquement l’exploitation du réseau DG–intégré, (ii) la propagation de ces incerti- tudes sur la réponse opérationnelle du système et le suivi du risque associé et (iii) les efforts de calcul intensif résultant du problème complexe d’optimisation combinatoire associé à l’intégration de DG renouvelable. Pour l’évaluation du système avec un plan d’intégration de DG renouvelable donné, un modèle de calcul de simulation Monte Carlo non–séquentielle et des flux de puissance optimale (MCS–OPF) a été conçu et mis en œuvre, et qui émule

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